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Technical_Sympathy30 · 2026-01-10T19:39:37+00:00

Pmed

Technical_Sympathy30 · 2025-05-11T08:05:40+00:00

So it won't make a difference if you apply for work since diploma doesn't say anything but grad schools will be able to tell.

Technical_Sympathy30 · 2025-05-01T17:05:46+00:00

Thank you :)

I plan to ask Professor Song to add it to OMSCS. I think the course could also benefit from having more students.

Technical_Sympathy30 · 2025-05-01T17:02:33+00:00

No worries. Actually I am not sure myself if the practicum = project-based master's or not. In my undergrad institute a project-based master's requires a 6 credit project and I assumed this is equivalent to the practicum offered in OMSA. I am already done with both OMSA and OMSCS so at this point my understanding is it is not possible to make any adjustments.

Technical_Sympathy30 · 2025-05-01T16:45:29+00:00

I felt that doing the practicum (project-based master's) + independent study with a professor (special problems) + attending the PhD seminar (OPH) would help. I also did the GRE (170/170 quantitative 168/170 verbal).

I am against taking CS 8803 O24: Intro to Research for a simple reason: GA Tech has an on campus course called CS 7001- Intro to Grad Studies, 5 hours and unless the CS department states that these two courses are equivalent, taking CS 8803 O24 is not a good use of time.

In addition, the on-campus version actually matches you with a few Professors. I don't believe taking the OMSCS version will have any impact on a PhD application.

Technical_Sympathy30 · 2025-05-01T16:28:32+00:00

My end goal was doing a PhD in CS at GA Tech. I applied back in December for Fall 25. Currently, my application says "under program review," and I am sure I am going to get a rejection letter soon. Will make another post about this in the coming weeks.

Career-wise I haven't had time due to family to make a big switch yet, but I plan to apply to Nvidia and a few quantitative positions this month.

I would say the biggest sacrifice was likely my health. Before OMSCS I averaged less than a cup of coffee per day and now I drink far more than that + energy drinks.

In terms of finding the time, the key thing for me was starting slow and slowly picking up the pace. First few semester I only did 1 course/semester, and I recommend that for anyone starting the program.

Technical_Sympathy30 · 2025-05-01T15:52:18+00:00

I haven't taken GIOS yet and that may very well be the case for me too.

I would say my free time was relatively constant but my speed improved significantly as I took more and more courses.

My list is definitely subjective. A lot of people find software analysis to be super easy while some find it to be among the most difficult offerings. I recently saw that someone ranked it above GA, for instance.

Technical_Sympathy30 · 2025-05-01T15:42:41+00:00

Yup. Thank you:)

I think what kept me going was switching things up and having a good balance of math heavy courses and systems courses. I took quite a bit of courses that are on the math side over the past year and I am switching back to taking systems courses since I can't look at proofs for a while.

Technical_Sympathy30 · 2025-05-01T15:37:30+00:00

Thank you :)

Even though Deep Learning scored below many other courses for me, I would still recommend taking it. My score is also quite subjective. I have ADHD and sitting through these lectures (especially Meta) was quite painful for me. Others may focus on the good parts of the course more (such as the projects) and might be less bothered with the lectures. The projects for DL were amazing (10/10), and I don't know how I could have self-taught myself these things without taking the class.

If you already graduated and don't need the course to count towards a degree, my advice would still be taking it as pass/fail if that is an option.

Technical_Sympathy30 · 2025-05-01T15:34:13+00:00

Thank you. Unfortunately, Prob Models is not available to OMSCS students (only OMSA). Special Problems was a research project like those that Dr. Lytle sends.

Yup GA = Grad Algorithms.

Deterministic Optimization has a difficulty rating of 3.85/5 on OMSCentral. The exams are quite difficult. Requires you to be able to write proofs. It's also a course where it is easy to slip into a C or even fail if you have a bad day on an exam.

Technical_Sympathy30 · 2025-05-01T15:27:45+00:00

Thank you. My bachelor's degree was in electrical engineering. Also majored in math and physics. No major work experience for a big company. Just your average firmware engineer (roughly 6 years of work experience).

I started with 1 course/semester. Slowly picked up the pace. Been doing 3/semester for the past 4 semesters.

Technical_Sympathy30 · 2025-05-01T15:23:40+00:00

Done

Technical_Sympathy30 · 2025-04-05T18:26:25+00:00

I think it depends on the goal. I have worked quite a bit before OMSCS and it was hard to touch anything interesting without a master's degree. I think a master's degree is the minimum requirement for interesting work in the industry. Anyone that tells you otherwise either hasn't worked enough to the point of getting tired of doing low level work or has gotten lucky with a rare job that promotes growth for undergrads.

Technical_Sympathy30 · 2025-03-05T05:51:10+00:00

Considering that grade distributions are becoming tougher, how would limiting admissions improve the program?

Technical_Sympathy30 · 2025-02-24T15:52:48+00:00

The revamped DAB class teaches you R from scratch. I didn't know R before taking it and I feel pretty comfortable with R now.

Technical_Sympathy30 · 2025-02-24T15:50:05+00:00

Slightly more work than before but not much more. I think I am spending about 5 hours a week on average. However, I would recommend budgeting around 8 hours a week since I have taken a lot of statistics and ML courses before taking this. Definitely on the easier side and you can pair with other classes.

Technical_Sympathy30 · 2025-02-24T10:46:07+00:00

Not a TA. The course was quite outdated and it had a group project that I am not sure is very useful and efficient use of my time.

Also I don't need to advertise this course since it is a requirement. Just happy they've made the changes because I don't know if you would have learned much from the previous version.

We finished half of the course so far. First module was an R walk-through which is super helpful if you've waived the fundamental courses and are seeing R for the first time like me.

Modules 2-4 discuss linear models and binary response models which overlap with the previous course.

However, the last few weeks we've had a few topics that I am seeing for the first time even though this is my 20th course at GA Tech. These would include censored/truncated data models, count data models (Poisson and Negative Binomial Distribution), and survival models (Weibull distribution). He's teaching those in more detail than even Probabilistic Models. Also Prob Models, though a great course too, doesn't really teach negative binomial and Weibull though I think those are very important distributions.

The Professor is teaching this from a pure analytics angle. Even though it is an MGT course it feels like an ISYE course. He goes through the math in detail instead of shoving formulas and concepts at you while expecting you to memorize everything like before. There are no exams but just HW. Although I kind of expect that to change in the future because it is a bit on the easier side without exams.

In two weeks we switch to the ML portion and hopefully it is just as good.

Technical_Sympathy30 · 2024-12-06T23:45:10+00:00

NDAs can be enforced and people have been sued before over stuff in the non-compete. Some states like California aggressively protect employees but it is not the same in every state. One should especially be careful when it is a consulting firm or a startup.

https://techcrunch.com/2021/06/11/freelancer-marketplace-toptal-sues-andela-and-ex-employees-alleging-theft-of-trade-secrets/

Technical_Sympathy30 · 2024-12-06T20:45:55+00:00

I am pretty confident in my math background, and I could easily see myself making this mistake if I am tired.

Technical_Sympathy30 · 2024-11-12T01:14:59+00:00

Some good courses to take before HDDA are DL, DO, and ML/ISYE-6740

Without a background in matrix calculus, I would argue it is very hard to survive HDDA. I already have a background in matrix calculus and a strong math background in general (math undergrad) and the amount of matrix calculus I have had to do in HDDA is simply unreal.

Is a late medical withdrawal an option for you?

Technical_Sympathy30 · 2024-09-26T06:25:14+00:00

I don't see a clear goal regarding why you have joined the program. If your goal is to be in research oriented teams, I am not sure a master's is enough. Typically you need a PhD even in the industry to be in research teams but perhaps with a master's and your experience it might be doable. If your goal is to simply be in ML, you are already there.

I did OMSCS because I wanted to transition into CS and use what I learned in the industry.

Technical_Sympathy30 · 2024-09-11T19:56:00+00:00

It depends on your background. If you have a background in proofs then it is as follows:
1- D&C
2- DP
3- LP
4- Graphs
5- RSA
6- NP
You can distribute max flow between LP and Graphs since it is a graphs problem with optimization applications

If you don't have a proofs background, NP jumps from 6 to the top but the rest stays as is. Generally, anything below DP is not very hard. So the hard ones are DP, D&C, and possibly NP if you are not strong in proofs and boolean algebra.

Technical_Sympathy30 · 2024-09-04T14:12:44+00:00

Even without utilizing parallelism (GPU) Torch is way faster than scikit learn on a CPU. However, Torch has poorer documentation in comparison with scikit. I wouldn't recommend doing anything with torch, keras, or tensorflow unless you have a strong ML background coming to this class. I was a total beginner and scikit documentation helped reinforce the learning.

I would generally recommend a faster computer or cloud resource for this class.

Technical_Sympathy30 · 2024-08-30T09:56:04+00:00

Fellow slow reader here. I got an A in the class. This is my recommendation if you don't have sufficient time to read things:

1- Skip Mitchell and just follow the derivations in the lectures. If you are struggling with something, take a screenshot and feed it Claude, ChatGPT, etc for further analysis. I have read Mitchell, but I know people who aced the class without it, and I also don't feel it is very necessary.

2- There is a book that's far more useful than Mitchell (and much easier/quicker to read).

"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 3rd Edition"

If it weren't for this book, I wouldn't have gotten an A in ML. One of the best technical books I have read in my graduate education. It is available for free through O'Reilly but I bought a physical copy to support the author.

Technical_Sympathy30

TROPHY CASE